“Mondays at the sun”:
Unemployment, Time Use,
and Consumption Patterns in Spain*
by
Namkee Ahn**
Juan F. Jimeno***
Arantza Ugidos****
DOCUMENTO DE TRABAJO 2003-18
August 2003
*
We are grateful to Dan Hamermesh and Stephen Jenkins for comments on earlier
drafts of this paper. Paper prepared for the IZA Conference of the International
Research Consortium in the Economics of Time Use, May 26-27, 2003, St. Gerlach,
The Netherlands.
**
FEDEA.
***
FEDEA and Universidad de Alcalá, Madrid.
**** Universidad del País Vasco, Bilbao.
Los Documentos de trabajo se distribuyen gratuitamente a las Universidades e Instituciones de Investigación que lo solicitan. No
obstante están disponibles en texto completo a través de Internet: http://www.fedea.es/hojas/publicaciones.html#Documentos de Trabajo
These Working Documents are distributed free of charge to University Department and other Research Centres. They are also available
through Internet: http://www.fedea.es/hojas/publicaciones.html#Documentos de Trabajo.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
1
Abstract
This paper is a first step towards learning about the implications of
unemployment with regard to the combination of consumption expenditures and
time use within households under the theoretical base on Becker’s (1964)
household production theory. We examine the Spanish experience, where the
unemployment rate was above 15% since the early 1980s up to the late 1990s
and, clearly, many unemployed workers were out of jobs involuntarily, hence
forced to adjust their time and consumption allocation patterns accordingly. Our
results, overall, seem consistent with the main prediction of the household
production theory. What we found is that time intensive commodities (passive
leisure, active leisure, housework and child care) are produced more in the
households with unemployed individuals. We also find that (with the exception
of single females) the proportion of consumption expenditures in time saving
goods is lower in the households with unemployed individuals.
Keywords: allocation of time, unemployment, consumption.
JEL Codes: J22.
Resumen
En este estudio analizamos los efectos del desempleo en las pautas de consumo
y en el empleo de tiempo, basándonos en la teoría de Becker (1964) sobre la
producción de los hogares. Examinamos el caso de España, donde la tasa de
desempleo ha sido alrededor de 15% desde el principio de la década de los 1980
hasta el final de la década de los 1990. Obviamente, muchos de los
desempleados estaban sin empleo contra su voluntad, por tanto, obligados a
ajustar consecuentemente sus decisiones sobre el uso del tiempo y el consumo.
Los resultados, en general, son consistentes con la predicción de la teoría de
Becker. Los hogares en los que hay miembros desempleados producen más
“mercancías” (commodities) intensivas en tiempo (ocio, tareas domésticas y
cuidado de niños). También encontramos que, salvo en el caso de las mujeres
solteras, la proporción de consumo dedicada a bienes y servicios que ahorran
tiempo en la producción de “mercancías” es más baja en los hogares donde hay
individuos desempleados.
Palabras clave: asignación del tiempo, desempleo, consumo.
Códigos JEL: J22.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
2
1. Introduction
In 2002 the biggest hit in Spanish cinema was Los lunes al sol (“Mondays
at the sun”). This film is about three unemployed men living in a town in
Northern Spain (Vigo) who spend their time sunbathing in the beach, drinking
beer, and complaining about globalization, capitalism, and trade union
weakness. It earned about 10 millions euros, attracted more than 2 million
viewers, and collected five Goya awards.1
This film nicely reflects a commonly held perception of unemployment,
which is considered a terrible waste of human resources and the most important
cause of deprivation in modern societies. However, although unemployed
workers may be deprived of some sources of income, they are not deprived of
their time. They still have available 24 hours a day, with the only difference that
they are restricted to allocate their time in activities other than market work. For
instance, some unemployed might take advantage of their unemployed period to
retrain themselves and improve their marketability and earnings potential, some
might dedicate more time on housework and care of other members of the
household; some might take more time for resting or enjoying more leisure,
maybe, sunbathing at the beach, etc. Presumably, changes in time allocation
after falling into unemployment will be carried out simultaneously along with
adjustments in the demand for some market goods.
These considerations regarding the use of time have been considered
relevant both at the macro and the micro levels. For instance, regarding national
accounting, there have been attempts to improve the welfare measure of a nation
by including in the measure of total production some items such as domestic
production (housework, care of children or elderly), health status, and the time
that the population spend on leisure. Moreover, at least since Benhabib et al.
(1991), home production and non-market activities are regarded as important
elements of models of the aggregate economy with important implications for
the performance of calibrated real business cycle models, for the interpretation
of the nature of aggregate fluctuations (Hansen and Wright, 1992, Greeenwood
et al., 1995), for the estimation of the intertemporal elasticity of substitution (see
Rupert et al. 2000), and for accounting for international income differences (see
Parente et al. 2000). Although there is a very wide empirical literature regarding
the estimation of parameters needed for the calibration of general equilibrium
models as far as market activities is concerned, there is much less information
1
The Goya awards are the Spanish versions of the U.S. Academy of Motion Picture Arts and Science’s Oscar
awards. This movie also attracted some attention in France and other European countries, but not much in the
US. Having being selected by the Spanish Academy of Cinematography to represent Spain in the competition for
the 2002 Oscar awards to the best film in foreign language, it did not make it into the last five.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
3
on parameters such as the elasticity of substitution between non-market goods
and time devoted to non-market activities.
In microeconomic research, as family economics is gaining some
momentum and more and better time use surveys are becoming available, there
is a growing research interest on other activities than market work, such as the
distribution of homework (Alvarez and Miles, 2003), and childcare (Hallberg
and Klevmarken, 2003, Ichino and Sanz de Galdeano, 2003), the use of leisure
time (Jenkins and Osberg, 2003), demand for formation and training (Fahr,
2003), transportation (Hertkorn and Kracht, 2002), and health care (Ruhm,
2003, Ruhm and Black, 2002).
This paper is a first step towards learning about the implications of
unemployment with regard to the combination of consumption expenditures and
time use within households. It examines the Spanish experience, where the
unemployment rate was above 15% since the early 1980s up to the late 1990s
and, clearly, many unemployed workers were out of jobs involuntarily. Hence,
this is a good scenario to investigate how the allocations of time and
consumption goods change with unemployment. Thus, our main goal is to
document how unemployed workers combine time and goods to produce
different commodities and how they differ from employed workers and those
individuals out of labor force. Given the restrictions on data availability, we
mainly rely on regressions using cross-sectional data to compare consumption
expenditure levels in different goods and time used in different activities
between employed and unemployed individuals in several types of households.
The paper contains five more sections. Section 2 reviews the literature on
the consequences of unemployment regarding changes in consumption and
welfare, including the predictions of models of household production which
explicitly consider time use and goods in the production of utility-enhancing
commodities. Section 3 lists the main activities and consumption categories to
be considered in the empirical analysis, while Section 4 describes the data and
Section 5 discusses the main results. Finally, section 6 contains some concluding
remarks.
2. Unemployment, consumption, and welfare: a review of the literature
Human well-being does not depend solely on goods and income, or on
market work only, but it also depends on other activities, such as domestic
production, housework, and care of children or other household members, and
on the amount of leisure enjoyed and knowledge acquired. Thus, when
evaluating the harm of unemployment, it is important to consider three factors:
i) the loss of production or income, ii) the increase in home production from the
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
4
additional time available, and iii) the direct impact of unemployment on
individual well-being.
In what follows we will focus on the second factor and stress that
alternative uses of time will determine the economic cost of unemployment,
which will be smaller than the derived loss of income if valuable non-market
commodities, such as domestic goods and care for children and other members
of the household, are produced with the additional time available. Even in the
case that increased non-market time were spent entirely on leisure or resting, the
welfare costs of unemployment should take into account of the value of these
activities perceived by the unemployed individuals. Before proceeding, we
provide a brief tour of the literature on the harm of unemployment.
This literature has followed several routes. One focuses on the loss of
income and consumption derived from unemployment, analysing consumption
behavior,
testing the permanent income hypothesis and searching for the
impact of insurance mechanisms (credit markets, the Welfare State,
interpersonal transfers, etc.) to explain consumption smoothing in the aftermath
of several shocks (fall in earnings, unemployment shocks, etc.). For instance,
Dynarski and Gruber (1997) find that in the US families do smooth consumption
to a large extent: a fall in 1 dollar in the head’s earnings implies a fall in 10
cents, at most, in total expenditures in consumption. Gruber (1997) analyzes the
role played by unemployment benefits at smoothing consumption in the US.
Castillo et al. (1999), using cross-sectional data, compare the difference between
the consumption levels of employed and unemployed workers in Portugal and
Spain, finding that it is larger in the former country where unemployment
benefits were less extended. Studies using longitudinal data are Browning and
Crossley (1998) for Canada, and Bentolila and Ichino (2003) for Spain, Italy,
US and the UK.
A less traveled route in recent years is the use of compensating variation
to measure the payment that would make an unemployed worker indifferent
between being employed at its desired number of hours and being unemployed,
as done in Hurd (1980). More often, empirical studies use survey data to relate
individual characteristics, including employment status, and subjective
“happiness” (see, for instance, Clark and Oswald, 1994, Korpi, 1997, Di Tella,
MacCulloch and Oswald, 2002). Within this branch of the literature, some
studies focus on an objective indicator of “well-being” by using some measures
of health status. Thus, there are estimates of the increase in the probability of
suffering some mental distress from the loss of labor earnings (see, for instance,
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
5
Bjorklund, 1985).2 These studies try to provide support to the view that
unemployment has a negative impact on individual’s “happiness” that goes wellbeyond that implied from the income loss.3
An alternative strand of the economic literature on the implications of
employment status for time and consumption expenditure allocations follows
Becker’s (1965) theory of household production. Under this approach,
individuals’ utility depends on commodities which are produced using both
consumption goods and time. Hence, the cost of unemployment should be
computed looking at how unemployment changes the combination of goods and
time used to produce the utility-enhancing commodities. Moreover, survey data
indicates that different activities have different impact on individuals’ mood,
with working being the activity, together with commuting, to which individuals
seem to assign a lower degree of satisfaction, while time spent on sex and other
leisure activities provide the highest levels of satisfaction (Layard 2003).
Although this approach has delivered many insights for issues in labor
economics and other areas (see Gronau, 1997, for a survey), there is a lack of
empirical studies aimed at measuring how households combine goods and time,
depending on employment status, and other households’ characteristics. Only,
recently, Gronau and Hamermesh (2003), using data from the US and Israel,
offer a first empirical characterization of the combination of time and goods to
produce utility-enhancing commodities within households and of the impact of
some demographic characteristics (namely, education levels and age) on the
relative time intensities of the alternative commodities.
The main reason for this unsatisfactory state of affairs is the lack of
microeconomic data on time use (based on time budget diaries, not on recall
questions) and consumption expenditures.4 Some countries carry out
longitudinal consumption expenditure surveys which can be used to measure
changes in consumption after some changes in households’ characteristics or
employment status of the members of the households (as in Dynarski and
Gruber, 1997, and Bentolila and Ichino, 2003). Time use surveys are less
common, and, when available, they are, almost exclusively, of a cross-sectional
nature. Moreover, their sample sizes are small, so that it is rarely feasible to
examine the impact of some particular variables on time use with the proper
controls. This particularly applies to employment status, since the proportion of
2
Other studies (for instance, Ruhm, 2003 and Ruhm and Black, 2002) claim, on the contrary, that health status is
countercyclical, since unemployment improves physical health through the reduction of smoking and drinking,
lower calories intake, fewer traffic accidents, and the rise of leisure time devoted to physical exercise.
3
This view is strongly endorsed by Layard (2003).
4
See Juster and Stafford (1991) for a survey of empirical findings and measurement problems in empirical
studies on the allocation of time.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
6
households’ heads who declare to be unemployed in this type of surveys tend to
be rather small (as it also happens in consumption expenditures surveys).
Following this approach, the extent to which individuals adjust their
consumption patterns when they become unemployed depends on several
factors. First, if the unemployment spell was anticipated, self-insurance is highly
likely, so that there would be no fall in disposable income. Alternatively, if it
was not anticipated, if there are liquidity constraints, if consumption and leisure
are non-separable, and if other sources of insurance (unemployment benefits,
family transfers, etc.) are not available, total consumption may fall when falling
into unemployment.5
As for time use, since unemployed workers experience increases in their
non-market time, they will reallocate time to optimize according to the new
circumstance. For instance, time intensive activities like domestic work can
substitute goods and services previously purchased in the market which are
relatively more expensive when the individual is unemployed. The theory of
household production stresses the scarcity of time as the main determinant of
distribution of expenditures and time between goods-intensive and timeintensive commodities. Thus, an increase in the shadow price of time raises the
relative price of time intensive commodities, and, hence, expenditures in goodsintensive commodities increase. Unemployment is associated to a fall in income
and an increase in time available for activities other than market work. Thus,
unemployed workers face a shadow price of time much lower than employed
workers so that they should devote more time to time-intensive commodities and
less to goods-intensive commodities. We also expect some differences among
unemployed in this regard depending on the degree of labor market attachment
of the individual. Hence, insofar as gender is a factor determining labor market
attachment, we would expect some differences in time use and expenditure
patterns between unemployed men and unemployed women. These
considerations guide the analysis of the differences between employed and
unemployed workers in their distribution of consumption and time use in the
next sections.
3. Selecting commodities, and allocating consumption expenditures and
time use
The first step in any empirical analysis of consumption and time use
patterns following Becker’s (1965) theory of household production is to define
relevant commodities and to identify the set of goods and activities which are
used to produce each commodity. Relating human activities to commodities is
5
See Bentolila and Ichino (2003) for a detailed discussion on the likely effects of unemployment on total
consumption expenditures.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
7
undoubtedly a challenging and controversial task. As an example, we can point
out to the serious conceptual issues regarding the distinction between
“productive” and “personal” activities, and which should be included in welfare
computations.6 Data availability from time budget and household expenditure
surveys also conditions the degree of detail which the researcher can achieve in
this regard.
Given the goal of this paper, that is, to measure and compare time use and
consumption patterns of individuals with different employment status, we go for
wide categories where sufficient variability can be observed. In particular, we
group commodities, time use, and expenditures in the following categories:
Commodities
Basic Personal Care
Housing (House work)
Child Care
Active Leisure
Passive Leisure
Money-Saving Activities
Time-Saving Expenditure
Time Use
Sleep, Personal hygiene,
Eating, Health care
Purchasing, Cooking,
Cleaning, Home
maintenance
Child care
Expenditure
Food, Apparels, Health
care, Personal hygiene
Rent (real or imputed),
House maintenance,
House equipment
Education for children,
Apparels for children
Training, Sports, Reading,
Training, Sports,
Job search, Social services,
Communication and
Gardening, Repairing
Reading
Conversation, Spectacles,
Alcohol, Tobacco,
Resting
Tourism, Spectacles
House work, Child care,
Adult care, Repairing
Kindergarten,
Restaurant, Domestic
service, Air trip
In the case of time use, time dedicated to market work and transport is not
included, while, in the case of expenditure, work related transport expenditures
are excluded. We analyze two additional categories, one for time use and the
other for expenditure, which are not mutually exclusive with other commodities.
The former is called money-saving activities which include time dedicated to
house work, repairing, child care and adult care. The latter is called time-saving
expenditure, which includes expenditures on nursery and kindergarten,
restaurant, domestic services and air trip.
Using this classification we depart from Gronau and Hamermesh (2003)
in several respects.7 First, we do not attempt to construct an exhaustive set of
commodities. Secondly, we distinguish active from passive leisure, as we expect
6
On this matter, see Joyce and Stewart (1999).
The list of commodities in Gronau and Hamermersch (2003) are: Sleep, Lodging, Appearance, Eating,
Childcare, Leisure, Health, Travel, and Miscellaneous.
7
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
8
unemployed workers to devote more resources to activities which may enhance
their employability, which are those included in the former category, so that
their use of leisure time would be qualitatively different to employed workers.
Third, we group some activities like sleep, personal hygiene, eating, and health
care, which Gronau and Hamermesh (2003) find to be relatively time-intensive,
into one category named as personal care. Finally, we examine two other
categories, money-saving activities and time-saving expenditure, which are not
exactly commodities but provide alternative measures of time use and
expenditure behaviors of individuals and households.
4. Data
Ideally, one would like to have data on both time use and consumption
expenditures from the same households to be able to analyze both allocation
decisions simultaneously. Unfortunately, we have no such data and are not
aware of its existence anywhere. Hence, we make use of two separate surveys,
one for time use and the other for consumption expenditures. The consumption
expenditures survey has a longitudinal structure, while the time use survey
provides only cross-sectional data. For consistency, we will not exploit the
longitudinal nature of the data on consumption expenditures and restrict
ourselves to examine the distribution of time and the distribution of
consumption expenditures separately.
The time use data is drawn from the Basque Country Time Budget Survey
(Encuesta de Presupuestos de Tiempo en el País Vasco, EPTPV) carried out in
1993 and 1998.8 The sample in each survey includes about 5,000 individuals of
the population of ages 16 or more. Only one person is selected for each
household. Each individual reports his/her time use (in minutes) during a certain
day using a time-diary method and her employment status one week before the
date of the survey. Of other household’s members only their basic sociodemographic information is collected, such as sex, age, marital status and
education level.
One advantage of the survey is that the sample size is sufficiently large to
provide us with a considerable number of unemployed workers. For example,
out of 9,925 individuals in the sample 861 persons were unemployed. One
disadvantage of the survey is that only one member in each household is
selected to fill in the time diary. Therefore, we do not have information on the
time allocation of other members of the household.
8
At the time this paper was written, the Spanish Statistical Office presented the preliminary results of a Time
Use Survey conducted in the whole country based on time diaries and following EUROSTAT’s guidelines.
Unfortunately, the individual data from this survey will not be made available to external researchers until mid2004.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
9
For our analysis we have selected two types of households, marriedcouple households and single person households where, in both cases, the
household head is below 60 years old. We exclude those whose reported time
diary is for Saturday or Sunday (about a half of the sample), due to a small
variation in the time use pattern by employment status, and unemployed
individuals who declared a strictly positive time devoted to market work. Our
final sample consists, after pooling observations for 1993 and 1998, of 1,100
married men, 1,059 married women, 93 single men and 72 single women. All of
the observations refer to individuals living in the Basque Country, a Northern
Spanish region.
Summary statistics for this sample can be found in Table A.1 in the
Appendix. For married individuals, around 50% of the observations are for
1998, while for single individuals more than 60% (63.8% for males and 79.4%
for females) are observations for this year. Time use is measured in minutes per
day. The mean value of market work time is 442.2 minutes for married males
and 147.2 minutes for married females. For singles, mean market work time is
362.1 for males and 328.8 for females. Leaving aside market work, the activities
where most time are spent are Personal Care (626.7 for married males, 645.6 for
married females, 614.0 for single males, and 657.2 for single females) and
Passive Leisure (176.5 for married males, 176.4 for married females, 222.1 for
single males, and 144.0 for single females). The activity eliciting less time
resources is child care (17.3 for married males, 49.92 for married females, 2.5
for single males, and 0.0 for single females).
Regarding demographic characteristics, mean age is similar across
household types (43.8 for married men, 42.95 for married women, 43.0 for
single men, and 41.8 for single women). The employment (unemployment) rates
of married men and married women turns out to be 87.0% (7.2%) and 36.9%
(8.3%), respectively. The corresponding values for single men and single
women are, respectively, 76.9% (13.2%), and 76.8% (11.7%). Since 1993 was a
drought in the business cycle while 1998 was at the middle of an expansion, the
observations for this year give much lower unemployment rates for all groups
(3.8%, 3.9%, 3.9% and 8.4%, for married men, married women, single men and
single women, respectively). Married males are relatively more educated than
married females, while single females are relatively more educated than single
males. In more than 20% of household with married individuals there are
children aged 0-4, and in around 16% of the same households there are children
aged 5-9. The mean number of children is about 1.7.
As for data on household expenditures we use the Spanish Continuous
Family Expenditure Survey (ECPF) which report employment status,
demographic characteristics of the family members, and information on
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
10
household consumption expenditure. It is a survey with a quarterly rotating
structure, with households observed for eight consecutive quarters. We use
annualized data for 1998 and 1999 where the amount in 1999 is discounted by
the inflation rate for that year to have the expenditure at 1998 price. Our final
sample, after applying the same selection conditions as in the time use data,
consists of 9,156 married-couple households, 279 single male households, and
285 single female households. The observations refer to households living
throughout Spain, not only in the Basque Country. Although we could have
selected from the observations of consumption expenditure survey only those
individuals in the Basque Country, this would have much reduced the sample
size at a small gain.9
Another structural difference between the time use data and the
expenditure data is that while the time use data refers to one individual for each
household, the expenditure data refers to each household. This causes no
problem for single person households, but for the multi-person households (in
our case, married-couple households) we have to take this difference into
account when interpreting the results.
Summary statistics of the data on consumption expenditures are in Table
A.2 in the Appendix. We make use of the proportion of expenditures devoted to
each commodity and also of the value of total expenditures (in units 100,000
Spanish, that is, 601.01 euros). Overall, married couple households devote a
higher proportion of the consumption expenditures to personal care than single
households, with single female households devoting about 6.5 percentage points
more than single male households to this category). Alternatively, single
households devote a higher proportion of their expenditures to housing. Passive
leisure is the third category receiving a higher proportion of expenditures, being
much higher for single male households (21.7%) than for married couple
households (16.1%) and single female households (9.4%). The proportion of
expenditures devoted to time saving goods and services is 10.7% in married
couple households, 14.9% in single male households, and 6.8% in single female
households. Finally, child care receives a 3% of expenditures in married couple
households.
As for demographic characteristics, the male unemployment rates turn out
to be 4.3% in married couple households and 12.5% in single male households.
As for females, the corresponding values are 6.3% and 9.3%, respectively. The
proportions of married couples with children aged 0-4 and aged 5-9 are 20.8%
and 17.8, while the mean number of children in the household is 1.8, similar
values to those found in the time use sample. As in the case of the time use
9
When we used only observations from the Basque Country, the results were similar but statistical significance
of estimated coefficients are often substantially reduced as its sample size is about 5% of the national sample.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
11
sample, married couple households are relatively less educated than single
individual households, being the difference in this regard between single male
and single female households almost negligible.
5. Results
We measure the differences in the distributions of households’ resources
(time and consumption expenditures) between employed and unemployed
individuals by performing Tobit regressions using the two cross-sectional
samples described above. In the Tobit regression for the allocation of time to
different activities, we include as independent variables, besides own
employment status, the spouse’s employment status, age and its square, the level
of education (primary, secondary and tertiary) and a dummy for year 1998, and,
in the regressions for married couple households, additionally, the number of
children in the households and two dummies for children aged 0-4 years and 5-9
years. The independent variables included in the Tobit regression for the
allocation of consumption expenditures to different activities are, in addition to
those in the regression for the allocation of time, household’s income and its
square.10 Since in this regression we are controlling for total household’s
income, the estimated differences in the allocation of consumption expenditures
between employed and unemployed individuals should be related to time
scarcity, not to an income effect. We perform regressions for the expenditures in
each category as a proportion of total expenditures.
One pending issue when interpreting the results of this regression analysis
is the likely endogeneity of the employment status. If unemployment is
involuntary, then the coefficient of the unemployment variable in the time use
regression reflects the response of the individual to an exogenous change in the
shadow price of time. Similarly, if unemployment is involuntary and
unanticipated, then the coefficient of the unemployment variable in the
consumption expenditures regression measures the adjustment in the allocation
of financial resources to an exogenous change in income, which is the net result
of the loss of labor earnings and the possible rise in non-earned income by
means of Welfare State benefits or interpersonal transfers within the household
or within an extended family network (see Bentolila and Ichino, 2003).
However, it is also conceivable that some individuals choose to change
their employment status (from employment to unemployment or to nonparticipation in the labor market) voluntarily in order to change their allocation
of time. This source of bias could be particularly relevant for the individuals
with a lower degree of attachment to the labor market, in particular, in the case
10
Unfortunately, the information on household income is not available in the time use sample.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
12
of females out of the labor force.11 It is also relevant in the case of married
couples who perform some division of labor, that is, where the male and the
female roles within the households are clearly delimited following traditional
patterns. Given the nature of our sample, there is not much we can do to dealing
with this source of bias, other than interpreting the corresponding estimated
coefficients as differences among individuals and households with different
characteristics rather than as a causal effect of unemployment on time use and
allocation of consumption expenditures. To make this interpretation more
informative we run separate regressions for two different types of households
(married couple household and single individual households) and also
distinguish by gender.
The results are reported in Table 1 for time allocation, and in Table 2 for
consumption expenditure allocation.12 In the following we discuss some main
results focusing on married men and women since the results for singles are less
robust due to their small sample size.
a)
11
Unemployed workers spend substantially more time on all activities other
than market work than employed workers. On the money-saving activities
such as housework and child (and adult) care, they spend about 3 and a
half hours more each weekday than employed workers. With respect to
consumption expenditure, total amount of consumption expenditure is
about 1,200 euros (about 10% of average annual expenditure) lower in the
households in which the husband is unemployed than in those the husband
is employed. In each type of expenditure, as a proportion over the total,
expenditures on personal care increases while those on active leisure
decreases if the husband is unemployed. Time-saving expenditure
decreases by 0.67 percentage points if the husband is unemployed. In
summary, there is a clear evidence that unemployed workers spend more
time on domestic commodities to substitute for market goods and enjoy
more leisure compared to the employed. As we do not know the value of
additional domestic commodities and leisure relative to that of the
reduced consumption of market goods, we cannot say much about overall
welfare costs of unemployment. However, we might conjecture that the
welfare loss from the reduced consumption (10%) is likely to be not too
high compared to the gains in domestic production and leisure. Of course,
these figures refer to contemporaneous losses and gains. To evaluate the
welfare loss or gain, one has to consider the entire period of
unemployment since they might vary over the unemployment duration.
Prodromidis (2003) shows that the selection bias is important in regressions explaining how British women
allocate time between market work, non market work, and leisure.
12
For simplicity, the Tables only contain the estimated coefficients for the main variables of interest:
employment status. The full sets of results are available upon request.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
b)
c)
d)
13
Between the unemployed and non-participants, there are some similarities
and some differences. For example, non-participants spend more time in
personal care and passive leisure than the unemployed while the opposite
is true with respect to the time spent on active leisure and domestic work
(for men only). With respect to the time spent on money-saving activities,
there are no significant differences between the two groups.
By gender, there are some interesting differences in the effect of
employment status on time use and consumption allocation. In time use,
the unemployed-employed difference is greater among men in leisure and
child care, while it is greater among women in housework and money
saving activities. This suggests that unemployed men use their increased
non-market time more on market-oriented commodities such as job search
while unemployed women use more time on home-oriented commodities
such as domestic production.
The differences in time use and consumption between employed and
unemployed also depend on the spouse’s labor market status. For an
unemployed man, if his wife is not working, his dedication on housework
or other money-saving activities increases less than otherwise, while his
leisure time does not depend much on his wife’s labor market status. For
an unemployed woman, the time dedicated on active leisure decreases if
her husband is also unemployed but the opposite is true with respect to the
time dedicated on housework.
A summary of the main differences between unemployed and employed
individuals with regard to the allocation of time and consumption expenditures
is presented in Table 3. Overall, they seem consistent with the main prediction
of the household production theory. As time is less scarce, we will expect that
unemployed spend more time in the production of commodities which are
relatively time-intensive. What we found is that time intensive commodities
(passive leisure, active leisure, housework and child care) are produced more in
the households with unemployed individuals. We also find that (with the
exception of single females) the proportion of consumption expenditures in time
saving goods is lower in the households with unemployed individuals.
6. Concluding Remarks
Unemployment is typically associated to a fall in labor earnings, to an
increase in non-earned income, and to a rise in non-labor time. Even if
individuals smooth consumption and total expenditures are unchanged, the
shadow price of time falls when unemployed. Hence, if the productions of
utility-enhancing commodities involve different time and good intensities, it is
very likely that the allocations of non-labor time and expenditure on
consumption goods differ with the employment status. While consumption
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
14
changes are usually taken as a measure of the painfulness of unemployment,
leisure and other alternative uses of time are disregarded in this type of
calculations.
Ideally one would like to observe how different individuals change their
allocation of consumption and time when going from employment to
unemployment. This requires the use of longitudinal data on consumption,
available for some countries, and on time use, which are not available in a panel
dimension. Thus, regarding time use, we can only compare unemployed and
employed individuals and, very often, time use surveys do not provide
sufficiently large samples to measure, with the proper controls, the time
allocations of individuals of different employment status. While we do not have
the longitudinal data to solve the first problem, we do have a sufficiently large
sample of unemployed individuals to overcome the second problem. The data
come from a relatively large time use survey done in the Basque Country, a
Northern region in Spain, for 1993 and 1998, when the unemployment rate was
between 15% and 20%, roughly.
Our results show that unemployed individuals devote their excess of non
labor time, in relation to employed workers, to passive leisure and domestic
work. They also increase, but to a lesser extent, the time intensity of the
production of commodities associated to active leisure and child care. We also
find that unemployed behave, in this regard, differently to non-participating
individuals, who have available the same amount of non labor time.
We are aware that our estimated differences in the allocations of
consumption expenditures and time between unemployed and employed cannot
be interpreted as a causal effect of unemployment, particularly in the case of
women. Nevertheless, we believe that they provide interesting evidence for the
measurement of home production, and, eventually, for the measurement of the
costs of unemployment using the perspective of the home production theory.
While waiting for richer data allowing observing simultaneously consumption
expenditures and time allocations for the same household, this evidence suggests
that there is much to be learned about the home production decisions and its
implications for a whole array of economic issues.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
15
References
Alvarez, B. and D. Miles (2003): “Gender effect on housework allocation:
Evidence from Spanish two-earner couples”, Journal of Population
Economics, vol. 16, 3.
Becker, G. (1965): “A Theory of the Allocation of Time”, Economic Journal, 75
(Sept.), 493-517.
Benhabib, J., R. Rogerson, and R. Wright (1991): “Homework in
Macroeconomics: Household Production and Aggregate Fluctuations”,
Journal of Political Economy, vol. 99 (6), pp. 1166-1187.
Bentolila, S. and A. Ichino (2003): “Unemployment and Consumption: Why are
Job Losses Less Painful than Expected Near the Mediterranean”, mimeo,
CEMFI.
Browning, M. and T. Crossley (1998): “Shocks, Stocks and Socks: Consumption
Smoothing and the Replacement of Durables during an Unemployment
Spell”, mimeo, University of Copenhagen.
Castillo, S., J.J. Dolado and J.F. Jimeno (1998): “The Fall in Consumption from
Being Unemployed in Portugal and Spain”, CEPR, Discussion Paper no.
1953.
Di Tella, R., R. J. MacCulloch and A. J. Oswald (2002): “The macroeconomics
of happiness”, University of Warwick working paper.
Dynarski, S. and J. Gruber (1997): “Can Families Smooth Variable Earnings?”,
Brookings Papers on Economic Activity, 1, 229-284.
Fahr, R. (2003): “I’m not Loafing, Darling, I’m Learning¡’ The Demand for
Informal Education” mimeo.
Greenwood, J., R. Rogerson and R. Wright (1995): “Household Production in
Real Business Cycle Theory”, in T. Cooley, ed., Frontiers of Business
Cycle Theory, Princeton University Press.
Gronau, R. (1997: “Home Production: A Survey”, in O. Ashenfelter and R.
Layard, eds., Handbook of Labor Economics, Vol. 1. North-Holland:
Amsterdam.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
16
Gronau, R. and D. S. Hamermesh (2003): “Time vs. Goods: The Value of
Measuring Household Production Technologies”, NBER working paper
9650, April.
Gruber, J. (1997): “The Consumption Smoothing Benefits of Unemployment
Insurance”, American Economic Review, 87(1), 192-205.
Hallberg, D. and A. Klevmarken (2003): “Time for children: A study’s of parent
time allocation”, Journal of Population Economics, vol. 16, 205-226.
Hansen, G. and R. Wright (1992): “The Labor Market in Real Business Cycle
Theory”, Quarterly Review, Federal Reserve Bank of Minneapolis, spring,
pp. 2-12.
Hertkorn, G. and M. Kracht (2002): “Analysis of a large scale time use survey
with respect to travel demand and regional aspects”, mimeo.
Hurd, M. (1980): “A Compensation Measure of the Cost of Unemployment to
the Unemployed”, Quarterly Journal of Economics, September, 225-243.
Ichino, A. and A. Sanz de Galdeano (2003): “Reconciling Motherhood and
Work: Evidence from Time Use Data in Three Countries”, mimeo.
Jenkins, S.P. and L. Osberg (2003): “Nobody to play with? The implications of
leisure coordination”, mimeo.
Joyce, M. and J. Stewart (1999): “What can we learn from time-use data?,
Monthly Labor Review, August, 3-6.
Juster, T. and F. Stafford (1991): “The Allocation of Time: Empirical Findings,
Behavioral Models, and Problems of Measurement”, Journal of Economic
Literature, 29, 471-522.
Layard, R. (2003): “Happiness: Has Social Science a Clue?”, Lionell Robins
Lectures.
Parente, S.L., R. Rogerson, and R. Wright (2000): “Homework in Development
Economics: Household Production and the Wealth of Nations”, Journal of
Political Economy, vol. 108(4), pp. 680-687.
Prodromidis, P.J. (2003): “Unpaid work, Non-work, and the Labour Supply:
Tobit or Heckit? Female Evidence from a 1999 British Survey”, mimeo.
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
17
Ruhm, C.J. (2003): “Healthy living in hard times”, NBER, working paper 9468.
Ruhm, C.J. and W.E. Black (2002): “Does drinking really decrease in bad
times?”, Journal of Health Economics, vol. 21(4) pp. 659-678.
Rupert, P., R. Rogerson, and R. Wright (2000): “Homework in Labor
Economics: Household Production and Intertemporal Substitution”,
Journal of Monetary Economics, vol. 46 (3) pp. 557-579.
18
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
Table 1. Effects of employment status on time use
Results from Tobit regressions
Dependent variable: Minutes per day devoted to each activity
Married Couple Households
Males
Females
Passive leisure
Unemployed
Out of labor force
Spouse unemployed
Spouse out of labor force
Active leisure
Unemployed
Out of labor force
Spouse unemployed
Spouse out of labor force
Domestic work
Unemployed
Out of labor force
Spouse unemployed
Spouse out of labor force
Personal care
Unemployed
Out of labor force
Spouse unemployed
Spouse out of labor force
Money-saving activities
Unemployed
Out of labor force
Spouse unemployed
Spouse out of labor force
Sample size
Single Individual Household
Males
Females
162.3 (9.1)
283.0 (18.7)
0.72 (0.1)
29.7 (7.4)
126.1 (7.3)
115.4 (15.1)
15.6 (0.8)
71.9 (5.8)
289.8 (3.9)
228.4 (3.5)
-42.9 (0.6)
332.9 (8.1)
89.4 (4.5)
66.5 (3.9)
2.01 (0.1)
-15.8 (1.8)
28.8 (1.3)
53.7 (5.8)
-56.9 (2.2)
-40.6 (2.8)
176.4 (1.8)
212.9 (2.5)
342.6 (5.0)
76.5 (1.9)
180.6 (10.7)
135.8 (9.2)
-26.4 (2.1)
-51.7 (7.0)
199.5 (10.1)
218.9 (25.0)
42.4 (1.8)
-50.3 (3.5)
45.7 (1.2)
-21.6 (0.7)
239.6 (5.0)
-3.18 (0.1)
66.9 (4.4)
141.4 (10.9)
5.85 (0.5)
7.29 (1.2)
39.3 (3.0)
60.0 (10.4)
0.14 (0.0)
-13.13 (1.4)
53.3 (0.9)
142.0 (2.8)
51.1 (0.8)
120.4 (3.3)
202.0 (13.1)
221.8 (19.4)
-40.7 (2.37)
-59.2 (5.9)
1,100
215.5 (10.0)
216.8 (16.8)
-13.2 (0.4)
-30.5 (1.5)
1,059
149.5 (3.7)
118.5 (2.7)
93.1 (3.1)
42.6 (0.8)
93
72
Child care (only couples with children)
Unemployed
64.3 (3.8)
Out of labor force
40.8 (1.7)
Spouse unemployed
-12.9 (1.1)
Spouse out of labor force
-12.8 (1.8)
Sample size
995
30.2 (1.7)
35.7 (4.0 )
34.1 (1.6)
-10.0 (0.6)
909
Notes: Additional regressors are age and its square, level of education, health status, dummy for year 1998, and,
for couples, number of children in the household, number of children in the household aged 0-4, number
of children in the household aged 5-9. Unsigned t-statistics are in parenthesis.
19
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
Table 2. Effects of employment status on consumption expenditures.
Results from Tobit regressions
Dependent variable: Expenditure in each category
(as percentage of total expenditure)
Married Couple
Household
Personal Care
Man unemployed
1.15 (2.2)
Man out of labor force
0.30 (0.6)
Woman unemployed
0.97 (2.1)
Woman out of labor force
1.02 (4.2)
Housing
Man unemployed
0.53 (0.8)
Man out of labor force
1.01 (1.7)
Woman unemployed
-0.11 (0.2)
Woman out of labor force
2.11 (7.2)
Passive Leisure
Man unemployed
-0.36 (0.8)
Man out of labor force
-0.21 (0.5)
Woman unemployed
0.33 (0.8)
Woman out of labor force
-1.05 (4.9)
Active Leisure
Man unemployed
-0.28 (1.8)
Man out of labor force
0.45 (3.1)
Woman unemployed
0.21 (1.5)
Woman out of labor force
-0.10 (1.4)
Child Care
Man unemployed
-0.21 (0.9)
Man out of labor force
-0.51 (2.3)
Woman unemployed
-0.08 (0.4)
Woman out of labor force
-0.34 (3.3)
Time-Saving Expenditure
Man unemployed
-0.67 (1.6)
Man out of labor force
-0.77 (2.0)
Woman unemployed
-0.42 (1.2)
Woman out of labor force
-1.44 (7.7)
Total Expenditure (in 100,000 pesetas (601 euros))
Man unemployed
-2.31 (3.0)
Man out of labor force
-1.48 (2.0)
Woman unemployed
-0.41 (0.6)
Woman out of labor force
0.14 (0.4)
Sample size
9,156
Single Male
Household
Single Female
Household
0.65 (0.2)
-3.21 (1.3)
-0.99 (0.3)
-0.02 (0.1)
15.1 (4.0)
-0.39 0.1)
5.97 (1.7)
9.13 (3.6)
-11.3 (3.2)
7.56 (2.3)
0.87 (0.4)
-2.10 (1.5)
2.01 (2.4)
-0.74 (0.9)
-0.74 (0.8)
-1.21 (1.8)
-13.9 (4.2)
2.54 (0.8)
2.82 (1.3)
-1.46 (0.9)
1.23 (0.6)
-0.92 (0.4)
279
1.70 (0.7)
-1.13 (0.6)
285
Notes: Additional regressors included are age and its square, level of education, total household income and its
square, and, for couples, number of children in the household, number of children in the household aged
0-4, number of children in the household aged 5-9. Unsigned t-statistics are in parenthesis.
20
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
Table 3. Summary of estimated differences between unemployed and employed
individuals in the allocation of time and consumption expenditures.
Married
Couples
Single
men
Single
women
Passive
leisure
Active
leisure
T: ++
C: 0
T: ++
C: -T: ++
C: 0
T: +
C: 0
T: ++
C: +
T: ++
C: 0
Domestic Personal
work
care
Housing
T: ++
T: +
C: 0
C: ++
T: 0
T: 0
C: ++
C: 0
T: ++
T: 0
C: ++
C: 0
Child
care
T: +
C: 0
Moneysaving
activities
T:++
Timesaving
expend.
C: --
T:++
C: -T:++
C: +
Notes: T, for time allocation. C ,for consumption expenditures allocation. ++ Large positive difference. +:
Moderate positive difference. 0: Not significant difference. -: Moderate negative difference. --: Large negative
difference.
21
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
Appendix. Summary Statistics
Table A.1. Time Use Sample
Married Males
Mean
Standard
deviation
Time Use (in minutes per weekday)
Market work
442.2
202.2
Domestic work
39.25
70.07
Child care
17.3
38.04
Personal care
626.7
98.49
Active leisure
43.91
67
Passive leisure
176.5
133.8
Money-saving
94.65
126.5
% with zero minutes
Domestic work
39
Child care
71
Personal care
0
Active leisure
51
Passive leisure
5
Money-saving
31
Demographic Characteristics
Age
43.81
9.056
Employed
0.87
0.336
Unemployed
0.072
0.259
Out of labor force
0.058
0.234
Spouse employed
0.379
0.485
Spouse unemployed
0.075
0.263
Spouse out of labor force 0.543
0.498
Children aged 0-4
0.215
0.411
Children aged 5-9
0.169
0.375
Number of children
1.681
0.962
Primary education
0.507
0.5
Secondary education
0.337
0.473
Tertiary education
0.156
0.363
Year98
0.492
0.5
Unemp*year98
0.038
0.19
Number of observations
1,100
Married Females
Standard
deviation
Mean
Single Males
Standard
deviation
Mean
Single Females
Standard
deviation
Mean
147.2
297.8
49.92
645.6
46.17
176.4
347.7
362.1
80.9
2.5
614.0
79.6
222.1
103.3
328.8
126.3
0.0
657.2
68.1
144.0
182.5
211.6
155
80.73
86.68
70.76
121.3
196.5
1
57
0
52
4
4
42.95
0.369
0.083
0.547
0.861
0.026
0.11
0.202
0.16
1.474
0.575
0.305
0.12
0.466
0.039
1,059
235.1
71.6
15.3
127.8
134.4
190.8
107.6
15
0
0
48
8
24
9.414
0.483
0.276
0.498
0.346
0.161
0.313
0.402
0.367
0.806
0.495
0.461
0.325
0.499
0.195
43.0
0.769
0.132
0.088
------------0.452
0.371
0.178
0.638
0.039
93
207.1
78.6
0.0
115.0
86.0
132.5
138.6
4
0
0
35
7
14
11.5
0.424
0.340
0.285
------------0.500
0.486
0.384
0.483
0.195
41.8
0.768
0.117
0.115
------------0.206
0.449
0.347
0.794
0.084
72
11.5
0.426
0.324
0.322
------------0.407
0.501
0.479
0.407
0.280
22
FEDEA- D.T. 2003-18 by Namkee Ahn et al.
Table A.2. Expenditure Sample
Expenditures as proportion of total
Personal care
Housing
Passive leisure
Active leisure
Child care
Time saving
% with zero expenditure
Personal care
Housing
Passive leisure
Active leisure
Child care
Time saving
Demographic Characteristics
Age
Man employed
Man unemployed
Man out of labor force
Woman employed
Woman unemployed
Woman out of labor force
With children aged 0-4
With children aged 5-9
Number of children
Primary education
Secondary education
Tertiary education
Earnings (pesetas, x100,000)
Number of Observations
Married Couple
Households
Standard
Mean
Deviation
Single Male
Households
Mean
Standard
Deviation
Single Female
Households
Standard
Mean
Deviation
0.284
0.342
0.161
0.048
0.030
0.107
0.200
0.418
0.217
0.042
--0.149
0.265
0.506
0.094
0.054
--0.068
0.106
0.123
0.088
0.030
0.039
0.077
0
0
0
1
25
3
43.927
0.904
0.043
0.053
0.402
0.063
0.534
0.208
0.178
1.764
0.619
0.173
0.209
27.629
9,156
0.122
0.184
0.169
0.037
--0.139
0
0
3
15
--13
8.737
0.295
0.203
0.224
0.490
0.243
0.499
0.406
0.382
0.988
0.486
0.378
0.406
14.054
43.131
0.744
0.125
0.132
------------0.495
0.144
0.361
18.372
279
0.119
0.153
0.083
0.040
--0.076
0
0
11
4
--26
9.708
0.437
0.331
0.331
------------0.500
0.351
0.481
11.206
46.698
------0.666
0.093
0.241
------0.476
0.171
0.353
16.729
285
10.162
------0.472
0.291
0.428
------0.500
0.377
0.479
9.933
23
RELACION DE DOCUMENTOS DE FEDEA
DOCUMENTOS DE TRABAJO
2003-18: ““Mondays at the sun”: Unemployment, Time Use, and Consumption Patterns in Spain”,
Namkee Ahn, Juan F. Jimeno y Arantza Ugidos.
2003-17: “Protecting Against Labour Market Risk: Employment Protection or Unemployment
Benefits?”, Tito Boeri, J. Ignacio Conde-Ruiz y Vincenzo Galasso.
2003-16: “What Social Security: Beveridgean or Bismarckian?”, J. Ignacio Conde-Ruiz y Paola
Profeta.
2003-15: “Forecasting the Dollar/Euro Exchange Rate: Can International Parities Help?", Simón
Sosvilla-Rivero y Emma García.
2003-14: “Employment Consequences of Restrictive Permanent Contracts: Evidence from Spanish
Labor Market Reforms”, Adriana Kugler. Juan F. Jimeno y Virginia Hernanz.
2003-13: “The underestimated virtues of the two-sector AK model”, Gabriel J. Felbermayr y
Omar Licandro.
2003-12: “The Effects of Employment Protection: Learning from Variable Enforcement”, Tito Boeri
y Juan F. Jimeno.
2003-11: “The effect of Structural Fund spending on the Spanish regions: an assessment of the 199499 Objective 1 CSF”, Angel de la Fuente.
2003-10: “Spanish Unemployment: The End of the Wild Ride?, Samuel Bentolila y Juan F.
Jimeno.
2003-09: “A New Test for Chaotic Dynamics Using Lyapunov Exponents”, Fernando FernándezRodríguez, Simón Sosvilla-Rivero y Julián Andrada-Félix.
2003-08: “Endogenous Policy Leads to Inefficient Risk Sharing”, Marco Celentani, J. Ignacio
Conde-Ruiz y Klaus Desmet.
2003-07: “Efectos a largo plazo sobre la economía andaluza de las ayudas procedentes de los fondos
estructurales: el Marco de Apoyo Comunitario 1994-1999”, Encarnación Murillo García
y Simón Sosvilla-Rivero.
2003-06: “The Role of Education vis-à-vis Job Experience in Explaining the Transitions to
Employment in the Spanish Youth Labour Market”, Cristina Fernández.
2003-05: “The Macroeconomics of Early Retirement”, J. Ignacio Conde-Ruiz y Vincenzo Galasso.
2003-04: “Positive Arithmetic of the Welfare State”, J. Ignacio Conde-Ruiz y Vincenzo Galasso.
2003-03: “Early retirement”, J. Ignacio Conde-Ruiz y Vincenzo Galasso.
2003-02: “Balance del sistema de pensiones y boom migratorio en España. Nuevas proyecciones del
modelo MODPENS a 2050”, Javier Alonso Meseguer y José A. Herce.
2003-01: “Convergence in social protection across EU countries, 1970-1999”, Simón SosvillaRivero, José A. Herce y Juan-José. de Lucio.
2002-26: “Temporary Employment and Segmentation in the Spanish Labour Market: an Empirical
Analysis through the Study of Wage Differentials”, María A. Davia y Virginia Hernanz.
2002-25: “Efectos económicos de las inversiones ferroviarias, 1991-2007”, José A. Herce y Simón
Sosvilla-Rivero.
2002-24: “Assessing self-assessed health data”, Namkee Ahn.
2002-23: “Especialización productiva y asimetrías en las fluctuaciones económicas en las regiones
europeas”, Jordi Pons Novell y Daniel A. Tirado Fabregat.
2002-22: “An Eclectic Approach to Currency Crises: Drawing Lessons from the EMS Experience”,
Reyes Maroto, Francisco Pérez y Simón Sosvilla-Rivero.
2002-21: “Migration Willingness in Spain: Analysis of Temporal and Regional Differences”,
Namkee Ahn, Juan F. Jimeno y Emma García.
2002-20: “¿Es relevante el trato fiscal diferencial en el volumen de ahorro de los individuos?”, José
A. Herce.